An annotator drawing boxes around objects in street photos on a large monitor in a calm office, the image soft and abstract, no readable interface

/Data Annotation

Data annotation outsourcing. Labels your models can learn from.

A managed annotation team that labels your images, text, audio and documents to your guidelines, inside your tools, with quality checks on every batch.

Training data
you can trust

A model only learns what its labels teach it. Data annotation is the human work of marking up raw data, drawing a box around each car in a photo, tagging the company names in a contract, rating which of two chatbot answers is better, so a model has examples to learn from and a standard to be tested against.

Data annotation outsourcing hands that work to a trained, supervised team instead of your engineers or an anonymous crowd. The team runs through partner centers we select and manage, in Mexico and Colombia or in the Philippines, India and Egypt, and works in your annotation platform under your guidelines.

You keep ownership of the data, the guidelines and the definition of a correct label. We run the people, the training, the quality checks and the feedback loop that keeps labels consistent as your project grows.

What we deliver

Image & Video Annotation

Bounding boxes, polygons, segmentation masks, keypoints and object tracking across video frames.

Text Annotation

Classification, sentiment, intent and named entity tagging in English and Spanish.

Audio Transcription

Transcription, speaker labels and timestamps, including Spanish and mixed English-Spanish speech.

Response Rating for AI

Ranking and rating model answers, search relevance judgments and writing reference answers for fine-tuning.

Document Data Extraction

Pulling fields from invoices, forms, receipts and contracts, and labeling layouts for document AI.

Content Moderation

Reviewing and labeling user content against your policy, with wellbeing support built into the schedule.

How it works

Scope & Guidelines

We review your data, label set and edge cases, and help turn them into written guidelines with examples.

Team & Access Setup

We select annotators for the task and language, sign NDAs and set up named accounts in your platform.

Pilot Batch

The team labels a small batch, you review it, and we fix the guidelines where people read them differently.

Calibration & Scale

Annotators are checked against your gold-standard items before they join production, then volume ramps up.

Ongoing Quality Loop

Spot checks, agreement checks and a weekly review of disagreements, feeding back into the guidelines.

What data annotation covers

Annotation is any task where a person adds the answer a model should learn to give. The data type changes the tools and the skills, but the job is the same: apply a written rule the same way, thousands of times, and flag the cases the rule doesn't cover.

Most projects fall into one of these groups:

Images and video: bounding boxes, polygons and pixel-level segmentation for computer vision, plus tracking objects from frame to frame.
Text: sorting messages by topic or intent, tagging names, places, products and amounts, and marking sentiment or tone.
Audio: transcribing speech, labeling who is speaking and when, and tagging background noise or language switches.
Search relevance and response rating: judging whether a result answers the query, ranking two or more AI answers, and writing better ones. This is the human feedback used to tune AI assistants, often called RLHF.
Documents: extracting fields from invoices, forms and contracts, and labeling page layouts so a model learns where things are.
Content moderation: labeling user posts, images or video against a safety policy, either to moderate live or to train a classifier.

How an annotation project runs

It starts with guidelines. Every label needs a written definition, examples of what counts and what doesn't, and a rule for the hard cases. Vague guidelines are the most common reason labels come back inconsistent, so we spend the first week on them with you.

Then a pilot batch. The team labels a small sample and you review it. The disagreements show where the guidelines are unclear, and fixing them now is far cheaper than relabeling later.

Before anyone works on production data, they pass calibration: labeling a set of gold-standard items where you've already agreed the right answer. Gold items keep appearing in the queue after that, so drift shows up early.

We also track inter-annotator agreement. In plain terms, we give the same items to two or more people and see how often they land on the same label. Low agreement on a label usually means the rule is unclear, not that people are careless, and it goes back into the guidelines at the weekly review.

Security and data handling

The work happens in your systems or the annotation platform you choose, not on our copies of your data. Annotators get named accounts with only the projects they need, and lose access the day they leave the project.

Everyone on the team signs an NDA before they see a single item. Data doesn't leave approved systems: no downloads to personal devices, no screenshots, no outside tools. Where the data includes personal information, we agree with you up front how it's masked, who can see it and what happens to it when the project ends, and we write that into the agreement rather than give you a general assurance.

Managed team or crowdsourcing marketplace

Crowdsourcing marketplaces split your work across many anonymous contributors. They're quick to start and suit simple, high-volume tasks with clear answers, where you can absorb some noise and filter it out with redundancy.

A managed team is the better fit when the task needs judgment, training or context: medical or legal text, long guidelines, response rating, bilingual content, or anything sensitive. The same people stay on your project, learn your edge cases, and can be asked why they labeled something the way they did. You also know who has seen your data.

Many teams use both: a crowd for the easy bulk and a managed team for the hard cases, quality review and gold-standard work.

Content moderation needs a wellbeing plan

Labeling harmful content takes a toll on the people doing it. If your project includes violent, sexual or abusive material, the plan has to account for that from the start, not after someone burns out.

We agree with you how much exposure each person gets per shift, rotate people between harmful and neutral queues, use blurring or grayscale where the tool allows, and agree before the work starts what counseling support and opt-outs from the hardest categories the partner center provides. It's the right way to treat people, and it also keeps judgment steady over months.

Nearshore or offshore for data annotation

Nearshore, in Mexico and Colombia, fits work that changes often and needs fast back-and-forth with your ML team during US hours: new guidelines, pilot batches, response rating where reviewers ask questions as they go. It's also the natural home for Spanish and bilingual English-Spanish data, labeled by native Spanish speakers who know how Latin American users actually write and talk.

Offshore, in the Philippines, India and Egypt, fits large, stable queues with settled guidelines, where work handed off at the end of your day comes back labeled the next morning. It costs less than nearshore, which costs less than onshore. Many projects split the two: guideline work and hard cases nearshore, steady volume offshore.

Nearshore data annotation Teams in Mexico and Colombia working your hours, in English and Spanish. Offshore data annotation High-volume labeling in the Philippines, India and Egypt. Onshore vs nearshore vs offshore How the three delivery models compare on cost, hours and handoffs. AI integration services Help deciding what to build and putting models to work in your business.

Questions to ask a data annotation partner

Ask how quality is measured. You want gold-standard checks, agreement between annotators and a regular review of disagreements, not a single accuracy figure on a slide.
Ask who the annotators are. Find out whether the same trained people stay on your project or whether work goes out to whoever is online.
Ask where the data lives. The best answer is your platform, named accounts and no copies outside it.
Ask what happens when the guidelines change. A good partner retrains, recalibrates and tells you which labels were made under the old rule.
Ask how moderation work is handled. If the answer doesn't mention exposure limits and support for annotators, look elsewhere.

Choose how
we run it

Nearshore · Mexico & Colombia

Nearshore data annotation, calibrated on your hours.

Nearshore data annotation from Mexico and Colombia: bilingual labeling, live calibration with your ML team, data security and when offshore fits.

Offshore · Philippines, India & Egypt

Offshore data annotation that runs on written guidelines.

Offshore data annotation from the Philippines, India and Egypt: which tasks fit, how overnight queues run, how to keep data safe and when nearshore fits.

Choose where
the work happens

Nearshore

Mexico & Colombia

Bilingual teams in US hours. The default for live, judgment-heavy work.

Offshore

Philippines, India & Egypt

Overnight, overflow and high-volume work in partner centers we manage and report on.

Onshore

On request

For roles that must be US-resident, arranged case by case.

Frequently asked questions

What is data annotation outsourcing?

It's handing the labeling of your AI training and evaluation data to an external, trained team. You define the labels and the rules; the team applies them to your images, text, audio or documents in your annotation platform, with quality checks along the way. The team runs through partner centers we select and manage in Mexico and Colombia, or in the Philippines, India and Egypt.

Is data annotation the same as data labeling?

In practice, yes. Both mean adding the information a model should learn from, such as a category, a box around an object or a rating. Some teams use "annotation" for richer markup, like segmentation or entity spans, and "labeling" for simple tags, but providers and buyers use the two terms interchangeably.

Which annotation tools can the team work in?

Whichever one you use. Most projects run in the client's own platform, whether that's a commercial annotation tool, an open-source one or something your engineers built. Annotators get named accounts with only the projects they need. If you don't have a tool yet, we'll talk through the options with you, but the choice and the account stay yours.

Can you annotate Spanish and bilingual data?

Yes, and it's one of the main reasons to annotate nearshore. Annotators in Mexico and Colombia are native Spanish speakers who also work in English, so they can tag intent in a Spanish customer message, transcribe speech that switches between languages, or rate a model's Spanish answer for tone as well as accuracy.

How do you keep labels consistent?

Clear guidelines, a pilot batch, calibration against gold-standard items before anyone works on production data, and regular checks of how often annotators agree with each other. Disagreements are reviewed weekly and turned into guideline updates. When a rule changes, the team is retrained and you're told which labels were made under the old version.

How is a data annotation project priced?

Usually per hour of annotator time or per labeled item, depending on how predictable the task is. The price moves with the data type, how long each item takes, how much review you need, the language and the delivery model: onshore costs most, then nearshore, then offshore. We quote once we've seen a sample of your data and your guidelines.

Send us a sample
of your data.

Tell us the data type, the labels you need and roughly how much there is. We'll reply within 24 hours with how we'd staff it and how the pilot batch would run.

Plan my pilot batch